The present embodiments relate to coronary computed tomography (CT)-based clinical decision support. Clinical decision making based on coronary CT angiography (CCTA) imaging is typically quite subjective. Currently, the decision to send patients to the catheterization laboratory is based on a subjective evaluation of anatomical features on the coronary CT angiography exam. Quantitative tools, such as quantitative lesion grading, total plaque volume, or calcium score, may be used in making this clinical decision. A large amount of other data may be used, but so much information often results in physicians and guidelines focusing on a sub-set of data, the image and a few quantitative tools. However, the current practices have shown low specificity in guiding patients to the catheterization laboratory, with a significant proportion of catheterization laboratory bound patients found to have no ischemia-causing lesions.
In today's clinical practice, the radiologist reports their findings based on a subjective interpretation of the CCTA examination to a treating physician. In certain instances, the radiologist uses some quantitative tools, such as quantitative lesion grading and total plaque volume, for the report. Given that there are multiple physicians involved, there may be delays in deciding upon treatment and less than all available information may be used.
One proposed solution to increase specificity is to use CT-based fractional flow reserve (CT-FFR) to better select patients who need to be referred to the catheterization laboratory. Over the last five years, non-invasive CT-FFR has been clinically validated in a several large studies. Several methods have been proposed for the computation of CT-FFR, namely 3D Computational Fluid Dynamics (3D CFD), hybrid reduced order CFD, lumped modeling, and machine learning (ML-FFR). Each of these methods have yielded very similar diagnostic performance in terms of sensitivity, specificity, positive and negative predictive value in well controlled prospective or retrospective clinical trials and clinical studies. In all these trials, a cut-off value of 0.8 is used for objective evaluation of the CT-FFR results against invasive FFR.
Due to the relatively low specificity of CT imaging, patients with intermediate lesions might be evaluated using CT-FFR. However, CT-FFR is expensive, and is currently not commonly utilized. The computation of CT-FFR typically requires a segmented anatomical model of the coronary anatomy, which is time consuming. Further, the computation of CT-FFR itself requires considerable computational effort. As a result, although CT-FFR is currently the most promising candidate for acting as a gatekeeper to the cardiac catheterization laboratory, the clinical utility of CT-FFR is hampered both by the time required to process one case as well as the cost of computing CT-FFR. Given the scarcity of use, there is no well-defined integration of CT-FFR for clinical decision making, especially since the head-to-head diagnostic accuracy against invasive FFR is moderate. Decision making based on coronary CT image interpretation and CT-FFR number may remain primarily subjective in nature.